Unsupervised, database-free RSSI Calibration for Indoor Navigation

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indoo.rs CaLibre
Unsupervised Database Free RSSI Calibration Indoor Navigation
Boxian Dong (indoo.rs, TU Wien), Thomas Burgess (indoo.rs), Hans-Berndt Neuner (TU Wien)
ENC 2017, Lausanne, May, 2017

2. CaLibre - Boxian Dong <boxian@indoo.rs> - ENC2017
indoo.rs GmbH.
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Positioning &
Navigation
Asset Tracking
Analytics

3. CaLibre - Boxian Dong <boxian@indoo.rs> - ENC2017
➔ indoo.rs localization relies on RSSI fingerprint maps
➔ SLAM Engine (SE)
◆ Generates initial radio maps
◆ Uses radio data from dedicated recordings
➔ SLAM Crowd Engine (SCE)
◆ Updates, improve and expand radio maps
◆ Uses radio data crowd sourced from navigating users
indoo.rs SLAM crowd engine™.
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Radio map
Update
Error estimation
CaLibre
SLAM
Localization

4. CaLibre - Boxian Dong <boxian@indoo.rs> - ENC2017
➔ RSSI must be made comparable
◆ RSSI only indicates relative power level
◆ RSSI characteristics differ between devices
◆ Crowd data combines many different devices
➔ CaLibre is our approach to this problem
◆ Calibrate all inputs in every job
● No need for a hard to maintain database
◆ Form tiles by grouping scans close to each other
● Scans in each group should have same power level
◆ Correlate differences all single tiles
● Create set of comparable samples
◆ Find linear relations from comparable samples
RSSI calibration.
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5. CaLibre - Boxian Dong <boxian@indoo.rs> - ENC2017
CaLibre: signal preprocessing.
signal preprocessing
Input recordings
building radio
maps
grouping scans
calibration sample
creation
offset and slope
regression
calibration
results
denoised signal
grouping index
scanned signal
building
statistic
comparable RSSIs
High Noise ± 10dB
Missing signals
Sliding window average
based noise reduction
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6. CaLibre - Boxian Dong <boxian@indoo.rs> - ENC2017
CaLibre: grouping scans in tiles.
signal preprocessing
Input recordings
building radio
maps
grouping scans
calibration sample
creation
offset and slope
regression
calibration
results
denoised signal
grouping index
scanned signal
building
statistic
comparable RSSIs
Group scans by
➔ Overlapping networks
➔ Having higher power than a
relative power threshold
(part of total visible area)
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7. CaLibre - Boxian Dong <boxian@indoo.rs> - ENC2017
CaLibre: grouping scans in tiles.
signal preprocessing
Input recordings
building radio
maps
grouping scans
calibration sample
creation
offset and slope
regression
calibration
results
denoised signal
grouping index
scanned signal
building
statistic
comparable RSSIs
➔ Low network overlap
➔ Low power threshold
➔ High network overlap
➔ High power threshold
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8. CaLibre - Boxian Dong <boxian@indoo.rs> - ENC2017
CaLibre: calibration sample creation.
signal preprocessing
Input recordings
building radio
maps
grouping scans
calibration sample
creation
offset and slope
regression
calibration
results
denoised signal
grouping index
scanned signal
building
statistic
comparable RSSIs
➔ High Noise
➔ Large fading effects
(bluetooth signal)
➔ Compute median and weight
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9. CaLibre - Boxian Dong <boxian@indoo.rs> - ENC2017
CaLibre: regression.
signal preprocessing
Input recordings
building radio
maps
grouping scans
calibration sample
creation
offset and slope
regression
calibration
results
denoised signal
grouping index
scanned signal
building
statistic
comparable RSSIs
➔ One sample per network per tile
➔ Using weighted Ridge regression
◆ Computationally cheap while robust to noise
➔ Linear fit
◆ a - slope a
◆ Δ80
- offset at -80dB
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10. CaLibre - Boxian Dong <boxian@indoo.rs> - ENC2017
CaLibre: test results.
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➔ Calibrate multiple 1~2 minutes recordings
➔ Summarize statistically calibration results between
same pair devices
➔ Comparing Calibre results with manual results

11. CaLibre - Boxian Dong <boxian@indoo.rs> - ENC2017
CONCLUSIONS:
➔ Calibrate RSSI reading between recordings
➔ Tiling parameters depend on radio
environments
➔ Less efficient in recovery Slope
CaLibre: conclusions.
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OUTLOOK:
➔ Calibration between recording and radio
map
➔ Tiling parameter further optimization
➔ Multiple recording calibration

12. CaLibre - Boxian Dong <boxian@indoo.rs> - ENC2017
Boxian Dong
Senior researcher
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